run_exp {passt}

Run simulations and analyze data

Description

Runs several simulations and returns correlative effect sizes between the frequency/total duration/single duration of each pattern and the output activation of the network for each pattern, respectively. Comparable to running an empirical experiment in judgments of frequency and duration and analyzing the data.

Usage

run_exp(
  frequency,
  duration,
  lrate_onset,
  lrate_drop_time,
  lrate_drop_perc,
  patterns = diag(length(duration)),
  number_of_participants = 100,
  cor_noise_sd = 0
)

Arguments

frequency

presentation frequency for each pattern in the matrix

duration

presentation duration for each pattern in the matrix

lrate_onset

learning rate at the onset of a stimulus

lrate_drop_time

point at which the learning rate drops, must be lower than duration

lrate_drop_perc

how much the learning rate drops at lrate_drop_time

patterns

matrix with input patterns, one row is one pattern

number_of_participants

corresponds with number of simulations run

cor_noise_sd

the amount of noise added to the final activations of the network, set to 0 if you do not want any noise

Value

data frame with three columns: f_dv, td_dv, t_dv which are the correlations between the frequency/total duration/single duration of each pattern and the activation of the network for each pattern, respectively.

See Also

run_sim

Examples

run_exp(10:1, 1:10, 0.05, 2, 0.2)